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Kant Kishore SharmaSr. Associate Business Consulting at SapientNitro

Suggest a Webinar from this Speaker

Kant is working as Senior Associate Business Consulting at SapientNitro and has over 8 years of experience in the IT industry. He has worked on different projects related to telecom, banking, travel and e-commerce. ... Full Profile

Kant is working as Senior Associate Business Consulting at SapientNitro and has over 8 years of experience in the IT industry. He has worked on different projects related to telecom, banking, travel and e-commerce. For the current project, Kant is working on designing a vacation recommender system for one of the major cruise liner.

Co Speaker:

Sivakumar Kalyanasunderam-Director of Technology at SapientNitro

Sivakumar is working in SapientNitro as Director of Technology and has over 20 years of experience in the IT industry. Sivakumar comes with rich experience in software development including development of products, frameworks and tools in leading companies. At SapientNitro Sivakumar has led several projects in e-commerce/ticketing and is currently working on a cruise booking engine including a vacation recommender system for a major cruise liner.

In simple words, Recommender Systems are software tools and techniques, for suggesting items to a user. Recommender systems can be implemented to show generalized or personalized recommendations to the user on your e-commerce/socializing websites. For recommendation, system gathers data relevant to the user and processes that data using different algorithms to make recommendations. Recommender systems have proven to be valuable means for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce.

In this webinar, we will discuss:

Why? An Introduction

Basic Techniques and Tools

Common examples of recommender systems in today’s world

Design and Evaluation

Learning in recommender systems

Conclusion

Key learning’s for you shall be:

Does the project you are working on, need a recommender system?

Who will be benefitted from this? You, user, client or all?

What data is accessible to you in your system and which approach you can use?